New `repeated_measures_d()`

[mattansb]

This PR introduces the new repeated_measures_d() function, thanks to the help of @bwiernik and @MatthewBJane (#372).

It can be installed via:

remotes::install_github("easystats/effectsize/@paired-d-2")

The function is fully documented (but @bwiernik and @MatthewBJane feel free to suggest edits), with an addition to existing SMD vignette, and effectsize(<t.test(paired = TRUE)>) support.

• [ ] Waiting to hear from Jeff Rouder if we can use his simulated puzzle data as our dataset. If I won't hear from him in a couple of days, I'll just simulate my own.

---

Demo of new functionality:

library(effectsize)

# Paired data -------

data("sleep")
sleep2 <- reshape(sleep, direction = "wide",
                  idvar = "ID", timevar = "group")

repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2)
#> d (rm) |         95% CI
#> -----------------------
#> -0.75  | [-1.17, -0.33]
#> 
#> - Adjusted for small sample bias.

# Same as:
# repeated_measures_d(sleep$extra[sleep$group==1],
#                     sleep$extra[sleep$group==2])
# repeated_measures_d(extra ~ group | ID, data = sleep)

# More options:
repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, mu = -1)
#> d (rm) |        95% CI
#> ----------------------
#> -0.28  | [-0.65, 0.09]
#> 
#> - Adjusted for small sample bias.
#> - Deviation from a difference of -1.
repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, alternative = "less")
#> d (rm) |        95% CI
#> ----------------------
#> -0.75  | [-Inf, -0.40]
#> 
#> - Adjusted for small sample bias.
#> - One-sided CIs: lower bound fixed at [-Inf].

# Other methods
repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, method = "av")
#> d (av) |         95% CI
#> -----------------------
#> -0.76  | [-1.13, -0.39]
#> 
#> - Adjusted for small sample bias.
repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, method = "b")
#> Becker's d |         95% CI
#> ---------------------------
#> -0.72      | [-1.20, -0.24]
#> 
#> - Adjusted for small sample bias.
repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, method = "d")
#> Cohen's d |         95% CI
#> --------------------------
#> -0.80     | [-1.30, -0.30]
#> 
#> - Adjusted for small sample bias.
repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, method = "z", adjust = FALSE)
#> d (z) |         95% CI
#> ----------------------
#> -1.28 | [-2.12, -0.45]

# d_z is the same as Cohen's d for one sample (of individual difference):
cohens_d(extra.1 - extra.2 ~ 1, data = sleep2)
#> Cohen's d |         95% CI
#> --------------------------
#> -1.28     | [-2.12, -0.41]

# Repetition data -----------

data("rouder2016")

# For rm, ad, z, b, data is aggregated
repeated_measures_d(rt ~ cond | id, data = rouder2016)
#> The rm standardized difference requires paired data,
#>   but data contains more than one observation per design cell.
#>   Aggregating data using `mean()`.
#> d (rm) |         95% CI
#> -----------------------
#> -0.80  | [-1.06, -0.53]
#> 
#> - Adjusted for small sample bias.

# same as:
rouder2016_wide <- tapply(rouder2016[["rt"]], rouder2016[1:2], mean)
repeated_measures_d(rouder2016_wide[, 1], rouder2016_wide[, 2])
#> d (rm) |         95% CI
#> -----------------------
#> -0.80  | [-1.06, -0.53]
#> 
#> - Adjusted for small sample bias.

# For r or d, data is not aggragated:
repeated_measures_d(rt ~ cond | id, data = rouder2016, method = "r")
#> d (r) |         95% CI
#> ----------------------
#> -0.26 | [-0.33, -0.18]
#> 
#> - Adjusted for small sample bias.
repeated_measures_d(rt ~ cond | id, data = rouder2016, method = "d", adjust = FALSE)
#> Cohen's d |         95% CI
#> --------------------------
#> -0.25     | [-0.32, -0.18]

# d is the same as Cohen's d for two independent groups:
cohens_d(rt ~ cond, data = rouder2016, ci = NULL)
#> Cohen's d
#> ---------
#> -0.25    
#> 
#> - Estimated using pooled SD.

# From t.test -----------------------------------------------

Tt <- t.test(sleep2$extra.1, sleep2$extra.2, paired = TRUE)

effectsize(Tt, type = "rm_b")
#> Becker's d |         95% CI
#> ---------------------------
#> -0.72      | [-1.20, -0.24]
#> 
#> - Adjusted for small sample bias.

^{Created on 2023-11-07 with reprex v2.0.2}

[mattansb]

@bwiernik I also added some unicode subscript letters just for you ;)

library(effectsize)

data("sleep")
sleep2 <- reshape(sleep, direction = "wide",
                  idvar = "ID", timevar = "group")

repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, method = "av")
#> d (av) |         95% CI
#> -----------------------
#> -0.76  | [-1.13, -0.39]
#> 
#> - Adjusted for small sample bias.

options(es.use_symbols = TRUE)
repeated_measures_d(Pair(extra.1, extra.2) ~ 1, data = sleep2, method = "av")
#> dₐᵥ   |         95% CI
#> ----------------------
#> -0.76 | [-1.13, -0.39]
#> 
#> - Adjusted for small sample bias.

[codecov[bot]]

Codecov Report

Merging #618 (bfd67c6) into main (448e39b) will increase coverage by 0.18%. The diff coverage is 94.03%.

:exclamation: Current head bfd67c6 differs from pull request most recent head 6e9f42c. Consider uploading reports for the commit 6e9f42c to get more accurate results

@@            Coverage Diff             @@
##             main     #618      +/-   ##
==========================================
+ Coverage   90.55%   90.74%   +0.18%     
==========================================
  Files          56       57       +1     
  Lines        3419     3564     +145     
==========================================
+ Hits         3096     3234     +138     
- Misses        323      330       +7     

Files	Coverage Δ
R/cohens_d.R	96.03% <100.00%> (+0.16%)	:arrow_up:
R/effectsize.R	96.77% <ø> (ø)
R/effectsize.htest.R	91.94% <100.00%> (+0.15%)	:arrow_up:
R/print.effectsize_table.R	97.39% <100.00%> (+0.02%)	:arrow_up:
R/rank_diff.R	81.15% <ø> (ø)
R/utils_ci.R	97.01% <ø> (ø)
R/repeated_measures_d.R	98.78% <98.78%> (ø)
R/common_language.R	94.71% <33.33%> (-0.90%)	:arrow_down:
R/utils_validate_input_data.R	85.26% <88.46%> (+1.93%)	:arrow_up:

[mattansb]

Alright thanks @MatthewBJane !